Foodish

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=true and openWorldHint=true; description adds that the tool routes to thousands of tools, fills arguments, and returns structured data with citation URIs. No contradiction, and the behavioral details go well beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with 'PREFER OVER WEB SEARCH' and examples follow. It is slightly verbose with redundant phrasing ('even if web search could also answer it'), but overall clear and well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers all necessary context for a single-parameter tool: what it does, when to use, what it returns (structured answer with citations), and how it connects to underlying sources. No output schema exists, but the description compensates fully.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a single 'question' parameter described as natural language. The tool description adds how the question is processed (routed, arguments filled), giving extra meaning beyond the schema's minimal description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool routes questions to the appropriate source among 2,520 tools and returns structured answers with citations. It distinguishes itself from web search by prefacing 'PREFER OVER WEB SEARCH', making its purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use: for current or historical data across many domains, and even when web search could answer. Provides examples and lists trigger phrases, leaving no doubt about appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds substantial behavioral context beyond annotations: it explains the internal resolution, classification into bet types, and fan-out to appropriate data packs. Annotations (readOnlyHint=true, openWorldHint=true) are consistent with this read-only, data-gathering behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph of about 5 sentences. It is informative and front-loaded with purpose and input types. While every sentence adds value, it is slightly longer than necessary; however, it remains clear and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema, the description adequately explains outputs (evidence packet + market-vs-model comparison). It covers input formats, processing logic, and intended use cases. The description is self-contained and sufficient for an agent to understand and correctly invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The description adds extra meaning: it explains how the market parameter is resolved (slug, URL, or text) and that depth controls evidence sources (quick=2-3, thorough=full). This goes beyond the schema's basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to research a Polymarket bet by pulling relevant Pipeworx data. It specifies accepted input types (slug, URL, question text) and outputs (evidence packet + market-vs-model comparison). This distinguishes it from siblings like ask_pipeworx and validate_claim, which are more general.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear use cases ('should I bet on X?', 'what does the data say?', 'is there edge?'). It implies this tool is preferred over general discovery tools, but does not explicitly state when not to use it or how it compares to alternatives like ask_pipeworx.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses data sources (SEC EDGAR/XBRL, FAERS, FDA), return format (paired data + citation URIs), and efficiency claim (replaces 8–15 calls). Annotations confirm readOnly and non-destructive, with extra context adding value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with core function, then use cases, then per-type details, then efficiency. Slightly long but every sentence adds value. Could be tightened without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers triggers, data sources, and return hints. Missing explicit output structure, but the description gives enough for an agent to understand what to expect. Annotations and schema cover all inputs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions. The main description adds context like 'tickers like AAPL, MSFT' and 'names like ozempic', explaining how to format the values parameter. This exceeds the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool compares 2–5 companies or drugs side by side, with specific use cases like 'compare X and Y'. It distinguishes the entity types and what data each pulls, making it unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit triggers (user says 'compare', 'vs', 'stack up') and contrasts with sequential calls. However, it doesn't explicitly state when not to use it or mention sibling alternatives, though comparison is unique among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that it returns 'the top-N most relevant tools with names + descriptions' and that it performs ranking, which goes beyond annotation information. No contradictions found.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with core purpose and usage advice. The list of example domains is somewhat lengthy but serves to convey breadth. Could be slightly more concise, but overall effective for an AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description compensates by stating what is returned ('top-N most relevant tools with names + descriptions'). Given moderate complexity (2 params, no enums), the description covers usage context, examples, and return behavior sufficiently.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%: both 'query' (natural language description) and 'limit' (max number, default 20) are described in the schema. The tool description does not add new semantic detail beyond giving example queries, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses specific verbs ('Find', 'browse', 'search', 'look up', 'discover') and clearly identifies the resource ('tools by describing the data or task'). It immediately distinguishes from sibling tools by positioning itself as a discovery tool to be called first, listing many domains to convey scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It also lists concrete use cases (SEC filings, financials, etc.) and provides a negative hint to discourage using it for a single answer.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, openWorldHint=true, destructiveHint=false, indicating a safe read operation. The description adds context beyond annotations by specifying the return content (filings, fundamentals, patents, news, LEI) and the inclusion of 'pipeworx://' citation URIs. It does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph but efficiently packs all necessary information. It front-loads the core purpose and quickly transitions to usage guidelines and return data. While it could be slightly more structured (e.g., bullet points), it remains concise with minimal redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (aggregating multiple data sources) and the absence of an output schema, the description reasonably covers return data types, input constraints, and usage guidance. It mentions what data is returned and provides examples, though a more detailed output description could enhance completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, providing baseline 3. The description adds meaning by clarifying that 'type' is currently limited to 'company' and that 'value' can be a ticker or zero-padded CIK, and explains why names are not supported—information that helps agents avoid errors.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get everything about a company in one call.' It lists specific data sources (SEC EDGAR, SEC XBRL, USPTO, news, GLEIF) and data types (filings, fundamentals, patents, news, LEI). It effectively distinguishes itself from siblings by noting it replaces calling 10+ individual pack tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage scenarios, such as when a user says 'tell me about X' or 'research Microsoft', and advises using resolve_entity first if only a name is available. It also includes a when-not-to-use hint ('names not supported') and suggests an alternative sibling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already set destructiveHint=true, indicating destructive action. The description adds behavioral context by specifying when deletion is appropriate (stale context, task completion, sensitive data), going beyond the annotation's binary nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences, front-loaded with the primary action ('Delete a previously stored memory by key'), and every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 parameter, no output schema), the description fully covers purpose, usage guidelines, and pairing with siblings, providing complete context for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the parameter 'key' has a clear description in the schema. The description does not add further semantic detail, so it meets baseline expectations without enhancement.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Delete a previously stored memory by key', which is a specific verb and resource. It also contrasts with siblings 'remember' and 'recall', clearly differentiating the tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage scenarios: 'when context is stale, the task is done, or you want to clear sensitive data'. It also advises pairing with 'remember' and 'recall', offering clear guidance on when to use and alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are present and the description adds important behavioral details: rate limit of 5 per identifier per day, that it's free, and doesn't count against tool-call quota. There is no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured, front-loading the purpose and usage in the first sentence. It provides necessary detail without verbose or redundant text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (3 parameters, nested object, enum) and the fact that no output schema is present, the description covers all necessary aspects: purpose, usage, formatting guidelines, rate limits, and impact. It is fully adequate for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All parameters are described in the input schema with full coverage. The description reinforces the meaning of the 'type' enum but does not add significant new meaning beyond what the schema provides. The baseline of 3 is appropriate as the schema already does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to provide feedback to the Pipeworx team about bugs, missing features, data gaps, or praise. It distinguishes itself from sibling tools like ask_pipeworx or discover_tools by being a feedback mechanism rather than a query or discovery tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly specifies when to use each feedback type (e.g., use 'bug' for wrong data, 'feature' for missing capabilities) and provides clear instructions on formatting (describe in terms of tools/packs, don't paste prompts). It also mentions the team's response and roadmap impact.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description adds significant behavioral context beyond annotations (two modes, search behavior, return structure) and does not contradict readOnlyHint or destructiveHint.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

~100 words, two paragraphs, front-loaded purpose, every sentence adds value. No redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, description explains return value (ranked opportunities with trade direction and reasoning). Covers both modes adequately for an AI agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already covers both parameters (100% coverage). Description elaborates on their behavior (walking child markets vs. searching across events), adding value beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool finds arbitrage opportunities via monotonicity violations, specifies two modes (event/topic), and distinguishes itself from siblings like polymarket_edges by explaining cross-event capability.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly explains when to use each mode with concrete examples (e.g., missing May≤June rule in single-event mode), but does not mention alternatives among sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description details the algorithm: scans top markets, groups by asset, fetches price history once, computes model probability, and ranks by edge. It also notes the V1 scope (crypto-price bets), adding value beyond the readOnlyHint annotation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a coherent paragraph of four sentences that front-loads the core action. It is concise but could be slightly tighter by trimming the model explanation without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, the description covers purpose, algorithm, use case, and limitations (V1, crypto-only). It explains return values (top N with edge and direction), and the annotations complement it well, making it complete for agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for all three parameters. The description reinforces the meaning (e.g., 'Top N' for limit) but does not add significant new information beyond the schema's descriptions and defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it scans high-volume Polymarket markets and returns those where Pipeworx data disagrees with market price, using a specific model. It distinguishes from sibling tools like 'polymarket_arbitrage' by focusing on edge detection for crypto-price bets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states it is built for the 'what should I bet on today' question, helping users discover opportunities without manual paging. While it does not provide explicit when-not-to-use guidance, the context is clear and allows agents to infer appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false. The description adds no new behavioral context beyond stating the result (a URL). With annotations present, this is minimally adequate but not enhanced.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It conveys the core purpose without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and simple annotations, the description is functional but minimal. It lacks details on the nature of the food images, frequency of updates, or any constraints, making it only moderately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters, so schema coverage is 100%. The description does not need to explain parameters, earning the baseline score of 4 for a parameterless tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Random food image URL' clearly states the tool returns a URL for a random food image, using a specific verb (random) and resource (food image URL). It distinguishes from sibling tools like random_by_category by being category-agnostic.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus siblings like random_by_category. The description lacks context for selection, leaving the agent without decision support.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description is consistent but adds no extra behavioral details. It does not explain what 'random image' entails or any potential side effects beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, short sentence that is front-loaded and contains no unnecessary words. Every word is necessary for conveying the core functionality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one enum parameter and robust annotations, the description is sufficiently complete. It does not explain the output format, but that is acceptable given the tool's straightforward nature and lack of output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description only mentions 'category' without explaining its role or enumerating accepted values. The schema provides the enum list, but the description adds no semantic context beyond the name itself.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Random image for category' clearly states the tool returns a random image, filtered by a specific category. This distinguishes it from the sibling tool 'random' which likely provides random images without category filtering.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when a user wants a random image from a specific category, but does not explicitly mention when not to use it or compare with alternatives. There is no guidance on selecting categories or distinguishing from other random tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true. The description adds useful context: scoping to identifier, and the dual behavior (retrieve vs. list). It does not explain behavior when key is missing (error vs. null), but overall it is transparent. Minor gap, so 4.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise (3 sentences) and front-loaded with the action. Every sentence provides essential information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only tool with one optional parameter and no output schema, the description covers retrieval, listing, scoping, examples, and sibling integration. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds value by explaining the effect of omitting the key (list all keys) and connecting it to the companion tools (remember, forget). This goes beyond the schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the verb ('Retrieve' or 'list') and the resource ('a value previously saved via remember' or 'all saved keys'). It explicitly distinguishes from sibling tools by mentioning pairing with 'remember' and 'forget'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description provides explicit when-to-use guidance ('Use to look up context the agent stored earlier...') and gives concrete examples (user's target ticker, address, research notes). It indicates how to list all keys ('omit the key argument') and is scoped to the agent's identifier.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations include readOnlyHint=true and destructiveHint=false, which the description aligns with. The description adds behavioral details: parallel fan-out to multiple sources, return structure (changes + count + URIs), and input format details. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that front-loads the main purpose. Each sentence adds value, covering use cases, sources, input formats, and output. Slightly verbose but no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides sufficient context for a tool with 3 well-documented parameters and no output schema. It explains the return structure, allowed entity type, and the fan-out behavior. Could mention potential delays or source availability, but otherwise complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All three parameters are fully described in the schema. The description adds valuable context: examples for `since` (ISO and relative formats), examples for `value` (ticker or CIK), and a suggestion for typical monitoring ('30d' or '1m'). This goes beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: retrieving recent changes for a company. It provides explicit use cases and example queries, and the parallelism to SEC EDGAR, GDELT, and USPTO distinguishes it from sibling tools like entity_profile or validate_claim.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit trigger phrases (e.g., 'what's happening with X?') and implicit context for when to use. It does not explicitly list alternatives, but the use cases are well-defined, and the tool's unique fan-out approach implies when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses key-value storage scoping and persistence details (24h anonymous, permanent authenticated) beyond annotations, with no contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four concise, front-loaded sentences with no redundancy; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Sufficiently covers all aspects for a simple write tool: purpose, usage, storage behavior, and integration with sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers both parameters with descriptions; description adds examples but no new semantic depth, appropriate given 100% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states verb 'save' and resource 'data to reuse later', distinguishing from siblings like recall and forget.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use examples (e.g., discovered ticker, address, preference) and mentions pairing with recall/forget, but lacks explicit when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so safety profile is covered. Description adds value by specifying return format (IDs plus pipeworx:// citation URIs) and scope (canonical/official identifiers). No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, front-loaded with purpose, minimal waste. Each sentence adds essential information: purpose, when-to-use, examples, and output description. Efficiently structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, description adequately explains return values (IDs, citation URIs) and workflow positioning (use before other tools). Sufficient for an agent to correctly select and invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema covers all parameters with descriptions (100% coverage). Description enhances with concrete examples of valid values for both params (e.g., 'Apple' → AAPL, 'Ozempic' → RxCUI). Provides pragmatic usage guidance beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool's purpose: looking up canonical identifiers for companies/drugs. It specifies identifier types (CIK, ticker, RxCUI, LEI) and gives concrete examples. Differentiates from siblings by noting it replaces 2-3 lookup calls.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs when to use: when a user mentions a name and needs official identifiers required by other tools. States 'Use this BEFORE calling other tools that need official identifiers.' Provides clear context for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context: returns verdict, extracted structured form, actual value with citation, percent delta, and mentions it replaces multiple sequential calls. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (5 sentences), front-loaded with purpose, and structured clearly: purpose, use-case trigger, scope, output summary. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 param, no output schema, good annotations), the description covers purpose, usage, domain, output structure, and internal process. It lacks detail on the 'extracted structured form' field, but otherwise is comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage for the single parameter 'claim'. The description adds concrete examples like "Apple's FY2024 revenue was $400 billion", which enhances semantic understanding beyond the schema's description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states precisely what the tool does: fact-check factual claims against authoritative sources, specifically company-financial claims via SEC EDGAR. It distinguishes from sibling tools like ask_pipeworx or compare_entities by being specialized for claim verification and consolidating multiple steps.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool (e.g., 'Is it true that…?', 'Verify the claim that…') and its domain (company-financial). It does not explicitly state when not to use it or mention alternatives, but the domain restriction is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.